云计算适应找什么样的工作(云计算出来找什么工作比较好)

Hello, I am very happy to answer your questions:
Careers that can be pursued by learning cloud computing
1. Cloud system administrator: configure and maintain systems, including basic cloud platforms, Resolve issues as they arise and plan for future cloud capability requirements.
2. Cloud computing engineer: Responsible for the formulation of cloud computing and data center project delivery plans and technical solutions, responsible for cloud infrastructure, cloud data migration, cloud disaster recovery and backup, and cloud reliability and security. Planning, design and implementation work.
3. Cloud computing development engineer: Responsible for the design and development of distributed software for cloud services.
4. Cloud computing architect: Lead the development and deployment of cloud computing projects to ensure the scalability, reliability, security, maintainability of the system, and achieve business and IT performance within budget Performance requirements.
5. Operation and maintenance engineer: Responsible for the implementation and operation and maintenance of cloud computing projects, and do a good job in network storage, database, backup, recovery, synchronization and other related work.

There are many kinds of jobs that you can do. Depending on the focus, you can include cloud computing operation and maintenance engineers, cloud computing development engineers, cloud computing technical support engineers, and then there are network engineers, storage engineers, etc. who are more technical. Data operation and maintenance engineers (DBA), operation and maintenance development engineers, technical implementation engineers, as well as system operation and maintenance engineers, desktop and monitoring engineers who prefer the most basic Linux. In addition to technology, if you like dealing with people, you can also apply for pre-sales engineer or project manager positions. Xinxiang Hongfu helps you find the right job.

With the development of the cloud era, big data has also attracted more and more attention. Cloud computing and big data have long been inseparable. Mastering cloud computing and big data also means mastering the common real-time and offline development frameworks of big data. You have the ability to design and develop architecture, and you can be qualified as a hadoop development engineer, spark development engineer, etc. Flink development engineer and other positions.
The following are the positions suitable for each stage:
Phase 1:
Basic knowledge (linux operation basics, shell programming, hadoop cluster environment preparation, zookeeper cluster, network programming), JVM Optimization (JVM running parameters, JVM memory model, use of jmap command, use of jstack command, use of VisualVM tools, JVM garbage collection algorithm, JVM garbage collector, Tomcat8 optimization, JVM bytecode, code optimization). After completing the above preliminary stage of learning, everyone will be able to complete common automation scripts for small and medium-sized enterprises.
Phase 2:
Hadoop environment construction 2.0 (hadoop original cluster construction, CDH version cluster construction), hdfs (hdfs entry, hdfs in-depth), mapreduce (mapreduce entry, mapreduce in-depth learning, mapreduce advanced), yarn, hive (hive installation, basic hive operations, advanced hive usage, hive tuning), auxiliary system tools (flume, azkaban scheduling, sqoop0), IMPALA, HUE, OOZIE. After learning this stage, everyone is basically qualified for offline related work, including ETL engineer, hadoop development engineer, hadoop operation and maintenance engineer, Hive engineer, data warehouse engineer and other positions.
Phase three:
kafka message queue, storm programming (storm programming, strom real-time Kanban case, storm advanced application). After completing the third phase of study, everyone will be qualified for Storm real-time computing related work, including ETL engineer, big data development engineer, Storm stream computing engineer and other positions.
Phase 4:
Project development (strom log alarm, strom router project development). After understanding the development of strom project, everyone will be qualified for stream computing development work, stream computing engineer, big data development engineer and other related positions.
Stage 5:
Scala programming (Scala basic syntax, object-oriented programming in Scala, pattern matching in Scala, introduction to actors in Scala, Actor practice, higher-order functions in Scala, implicit conversions and Implicit parameters, Akka programming practice), Spark (Spark overview, Spark cluster installation, SparkHA high-availability deployment, Spark program, RDD overview, creating RDD, common RDD operator operations, RDD dependencies, RDD caching mechanism, DAG Generation, spark checkpoint, SparkSQL overview, DataFrame introduction and comparison with RDD, DataFrame common operations, DataSet introduction, executing SparkSQL queries programmatically, SparkonYarn introduction, sparkStreaming overview, SparkStreaming principle, DStream related operations, Dstream operation practice, sparkStreaming Integrate flume in practice, sparkStreaming integrates kafka in practice), Hbase (hbase introduction, hbase deployment, hbase basic operations, hbase filters, hbase principles, hbase advanced). After completing the fifth stage of learning, everyone will be qualified for Spark-related jobs, including ETL engineers, Spark engineers, Hbase engineers, etc.
Stage six:
User portrait (user portrait overview, user portrait modeling, user portrait environment, user portrait development, hive integration with hbase, hbase integration with phoenix, project visualization). After completing the actual big data Spark project, you can be competent in Spark-related work, including ETL engineer, Spark engineer, Hbase engineer, user portrait system engineer, and data analyst.
Stage 7:
Flink (Flink entry, Flink advanced, Flink e-commerce project). After completing the study of Flink real-time computing system, everyone will be qualified for Flink-related jobs, including ETL engineer, Flink engineer, big data real-time development engineer and other positions.
Stage 8:
Introduction to machine learning (machine learning concepts, mathematical foundations of machine learning), machine learning language basics (Python language, Python data analysis library practice, user portrait label prediction practice), Integrated learning algorithms, building brain drain models, data mining projects, recommendation systems, and actual CTR click rate estimation. After completing the final study, you will be able to be competent in machine learning, data mining and other related work, including recommendation algorithm engineers, data mining engineers, and machine learning engineers, filling the gap created by the rapid growth of talents in the field of artificial intelligence.

The careers you can pursue by learning cloud computing are:
1. Cloud system administrator: configure and maintain systems, including basic cloud platforms, solve problems that arise, and plan for future cloud deployments skill requirements.
2. Cloud computing engineer: Responsible for the formulation of cloud computing and data center project delivery plans and technical solutions, responsible for the planning and design of cloud infrastructure, cloud data migration, cloud disaster recovery and backup, cloud reliability, security, etc. and implementation work.
3. Cloud computing development engineer: Responsible for the design and development of distributed software for cloud services.
4. Cloud computing architect: Lead the development and deployment of cloud computing projects to ensure the scalability, reliability, security, and maintainability of the system, and meet business and IT performance requirements within budget.
5. Operation and maintenance engineer: Responsible for the implementation and operation and maintenance of cloud computing projects, and do a good job in network storage, database, backup, recovery, synchronization and other related work.
Positions available for big data:
1. Big data system R&D engineer: Responsible for big data system research and development, including large-scale unstructured data business model construction, big data storage, database construction, and database optimization Architecture, database center design, etc., and at the same time, they are also responsible for the daily operation of the data cluster and system monitoring. This type of talent is a must for any organization that builds a big data system.
2. Big data application development engineers: Responsible for building big data application platforms and developing analytical applications. They must be familiar with tools or algorithms, programming, optimization and deployment of different MapReduce. They develop various applications based on big data technology. Programs and Industry Solutions. (This is the best starting point for training in the industry).
3. Big data analysts: Mainly engaged in data mining, using algorithms to solve and analyze problems, so that the data can reveal the truth. At the same time, they also promote the continuous updating of data solutions.
4. Data visualization engineer: Responsible for using graphical tools and methods to clearly reveal the complex information in the data and help users better develop big data applications in the collected high-quality data
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